Monetizing financial brokerage data

ABSTRACT

Method and systems for monetizing financial brokerage accounts are disclosed. One aspect for certain embodiments includes mining data from financial brokerage accounts and monetizing the mined data and providing to the customer an unlimited number of free trades for an unlimited period of time.

CROSS-REFERENCE TO RELATED APPLICATIONS

This present application is a continuation of U.S. patent applicationSer. No. 16/232,731 filed Dec. 26, 2018, which is a continuation of U.S.patent application Ser. No. 14/589,302 filed Jan. 5, 2015, which is acontinuation of U.S. patent application Ser. No. 13/489,301 filed Jun.5, 2012, which claims priority to U.S. Provisional Application No.61/636,508 filed Apr. 20, 2012. All applications are incorporated byreference in their entirety herewith.

TECHNICAL FIELD

The disclosed embodiments relate generally to data analytics. Moreparticularly, the disclosed embodiments relate to monetizing financialbrokerage data.

BRIEF DESCRIPTION OF THE DRAWINGS

For a better understanding of the aforementioned aspects of theinvention as well as additional aspects and embodiments thereof,reference should be made to the Description of Embodiments below, inconjunction with the following drawings in which like reference numeralsrefer to corresponding parts throughout the figures.

FIG. 1 illustrates a high-level overview of the monetization model basedon analytics of user brokerage data and other data, according to certainembodiments of the invention.

FIG. 2 further illustrates some forms of user input data, according tocertain embodiments of the invention.

DESCRIPTION OF EMBODIMENTS

Methods, systems, user interfaces, and other aspects of the inventionare described. Reference will be made to certain embodiments of theinvention, examples of which are illustrated in the accompanyingdrawings. While the invention will be described in conjunction with theembodiments, it will be understood that it is not intended to limit theinvention to these particular embodiments alone. On the contrary, theinvention is intended to cover alternatives, modifications andequivalents that are within the spirit and scope of the invention. Thespecification and drawings are, accordingly, to be regarded in anillustrative rather than a restrictive sense.

Moreover, in the following description, numerous specific details areset forth to provide a thorough understanding of the present invention.However, it will be apparent to one of ordinary skill in the art thatthe invention may be practiced without these particular details. Inother instances, methods, procedures, components, and networks that arewell known to those of ordinary skill in the art are not described indetail to avoid obscuring aspects of the present invention.

According to certain embodiments, detailed personal information andfinancial information of a user of a financial brokerage account iscross referenced with the user's investment patterns, investmentpreferences and other online behavior of the user for purposes of dataanalysis and monetization.

According to certain embodiments, analytics of the user's financialbrokerage account information and brokerage transactional behavior canbe profiled to correlate dynamically with current world events totrigger investment offers and or commercial sales offers and or serviceoffers to the user.

According to certain embodiments, analytics of a given user's financialbrokerage account information and brokerage transactional behavior canbe profiled for searches for additional data from public records on thegiven user or on other users with profiles similar to the given user toprovide for further data analytics on the given user to triggerinvestment offers and or commercial sales offers and or service offersto the user.

According to certain embodiments, data analytics are used for buildingprofiles for individual financial brokerage account holders as well asbuilding profiles that are based on brokerage data and or other thirdparty data that meet certain criteria for purposes of monetization invarious industry segments.

According to certain embodiments, value may be returned to users of thefinancial brokerage account through free trades, money back, credits,coupons, promotional or other rewards programs. Free trades can also beprovided to referral clients referred by a customer (user) of thefinancial brokerage account.

According to certain embodiments, a user need not fund his/her accountin the financial brokerage. In other words, a user can open a“non-funded” brokerage account. The user can merely provide his/herdetailed personal information and financial information to the financialbrokerage. Such information can be used for data analysis andmonetization.

According to certain embodiments, data analytics of the user datainclude building various data taxonomies, user classification profiles,profile clusters, and rules engine for predicting behavior or thatsatisfies criteria for various market segments.

According to certain embodiments, free trades include commission-freetrades. The customer is not charged a sales charge or commissionassociated with the customer's brokerage transaction.

According to certain embodiments, free trades include spread-freetrades. In such trades, the spread between the bid and ask price iseliminated.

According to certain embodiments, free trades include one or more of thefollowing: 1) no load on mutual fund transactions, 2) eliminatingprincipal's fees on fixed income securities transactions, and 3)eliminating principal's fees on equity securities transactions.

According to certain embodiments, value can be returned to theuser/customer of the financial brokerage account by providing theuser/customer unlimited number of free trades. Also, value can bereturned to the user/customer of the financial brokerage account byproviding the user/customer unlimited number of free trades for anunlimited period of time.

According to certain embodiments, in addition to providing free trades,value may be returned to users/customers of the financial brokerageaccount by providing one or more of the following: 1) free checkwriting, 2) free bill payments, 3) eliminating annual fees on thecustomer's account, 3) eliminating transfer fees for the customer'saccount, 4) eliminating fees for debit card transactions, 5) eliminatingfees for credit card cash advances, 6) eliminating fees on thecustomer's retirement account, 7) eliminating maintenance fees foremployee stock option plans, 8) eliminating wire transfer fees for thecustomer's account, 9) eliminating ATM fees for the customer's account,and 10) eliminating ACH fees for the customer.

According to certain embodiments, in addition to providing free trades,value may be returned to users/customers of the financial brokerageaccount by eliminating fees for market data services. Market dataservices can include providing real time quotes and/or streaming quotesto the customer/user of the financial brokerage account.

According to certain embodiments, value can be returned to a user whohas an account (whether funded or unfunded) in the financial brokerageby providing an online shopping site associated with the financialbrokerage and where the brokerage user can shop and accumulate credits.Further, data analysis can be performed on the user's shopping behaviorin conjunction with the user's personal and financial information. Userscan shop by redeeming credits/coupons provided by the financialbrokerage as a return of value to the user.

According to certain embodiments, a user/customer of the financialbrokerage account may be any one of the following non-limiting entities:an individual, a for-profit business entity, a charitable organization,a scholastic institution, a public works entity, a government entity, asovereign wealth fund, a trust fund, or an institutional investor.

FIG. 1 illustrates a high-level overview of the monetization model basedon analytics of user brokerage data and other data, according to certainembodiments of the invention. In FIG. 1, the user/client 102 of afinancial brokerage account, in the course of using the brokerageservices, inputs user input data 104, transactional data 106, andbehavioral data 108. User input data 104, transactional data 106, andbehavioral data 108 are described in greater detail herein. According tocertain embodiments, user input data 104, transactional data 106, andbehavioral data 108 are stored in an information repository 110. Dataanalytics is performed on the data stored in the information repository110 to generate monetizable data information 112. Data information 112is monetized to generate revenue for the financial brokerage company118. In addition, traditional financial brokerage activity 116 alsogenerates revenue for the financial brokerage company 118. According tocertain embodiments, the financial brokerage company 118 returns value120 to the user. Some non-limiting examples of value returned to theuser include free trades, money back, credits, coupons, promotional orother rewards programs. The data analytics performed on the data storedin the information repository 110 is described in greater detail herein.

FIG. 2 further illustrates some forms of user input data. FIG. 2 showsthat user input data 202 includes new account data 204, margin accountdata 206, options account data 208, retirement data 210, ACH data 212,investment access data 214, certification of investment power(corporation) data 216, certification of investment power (trust) data218, RIA data 220, and tax data 222, according to certain embodiments.Such data is described in greater detail herein.

New Account data: According to certain embodiments, some non-limitingexamples of new account data include the following.

-   -   Account Type—Individual, Joint, Custodial, Retirement (IRA/Roth        IRA/401k/Sep IRA), Entity (LLC, S CORP, C CORP, Investment Club,        Partnership)    -   Name    -   Home Address    -   City    -   State/Territory    -   Zip Code    -   Social Security number/Tax ID number    -   Date of Birth    -   Marital Status    -   Country of Citizenship    -   Primary ID Document    -   Primary ID Document Number    -   Home Phone    -   Cell Phone    -   Fax Number    -   Email Address    -   Twitter information    -   Facebook information    -   Other Social Media information    -   Occupation    -   Employer Name    -   Employer Address    -   Employer City    -   Employer State    -   Employer Zip    -   Annual Income    -   Net Worth—Excluding Home    -   Liquid Net Worth    -   Tax Bracket    -   Spouses Name, First, Last    -   Spouses Occupation    -   Spouses Employer    -   Spouses Date of Birth    -   Spouse Email Address    -   Number of years as investor    -   Number of dependents (including self)    -   Clients Investment experience (none, limited, average,        extensive, Registered Investment Advisor)    -   Source of Funds (Business, Self Employment, Gift, Inheritance,        Investment Income, Sale of Asset, Savings, Settlement, Wages,        Income)    -   Account Source—How did you hear about us?    -   Investment Objective (Preservation of Principal, Conservative        Growth, Aggressive, Speculation)    -   Is primary client or immediate family member affiliated or        employed by another Broker Dealer member firm?    -   If yes, indicate Firm name and position (proper authorization        must be obtained from the member firm)    -   Is primary client or an immediate family any of the following:        director, shareholder with 10% or more of stock, or a        policy-making executive officer of a publicly traded company? If        yes, indicate company and position    -   Is the account traded by a third party? yes, state the        name/address of the third party trading authority form may be        required)    -   If Will a third party be provided with: Duplicate confirmations,        Duplicate Statement    -   Cash Sweep Selection (None, WIN/CIP, Prime MMF Sweep)    -   Standing Instructions        -   Buy (Hold in Firm Name, Transfer and Ship, Transfer and            Hold)        -   Sell (Hold Proceeds in Account, send Proceeds—net Sell, Send            proceeds as Designated—No netting)        -   Dividend/Interest (Hold Funds, Pay Monthly, Pay Weekly, Pay            Daily)    -   Other Financial Institutional accounts (Savings, Checking,        Credit Cards, Brokerage, Mortgage)

Margin Account data: According to certain embodiments, some non-limitingexamples of margin account data include the following.

-   -   Authorization and acknowledgement of terms and conditions        -   1. Name and address        -   2. Account owner signature        -   3. Title (e.g., President, Partner, Trustee, Custodian)        -   4. Phone number        -   5. Occupation        -   6. Employer        -   7. Financial advisor signature

Options Account data: According to certain embodiments, somenon-limiting examples of options account data include the following.

-   -   Trading Authority?    -   Date of ODD Delivery    -   Options Experience        -   Covered Calls______        -   Buying______        -   Spreads______        -   Uncovered Puts with Margin______        -   Uncovered Puts with Cash______    -   Type of Options Trading desired        -   Covered Calls        -   Buying        -   Spreads        -   Uncovered Puts with Margin______        -   Uncovered Puts with Cash    -   Authorization and acknowledgement of terms and conditions        -   Name and address        -   Account owner signature        -   Title (e.g., President, Partner, Trustee, Custodian)        -   Phone number        -   Occupation        -   Employer        -   Financial advisor signature

Retirement Account data: According to certain embodiments, somenon-limiting examples of retirement account data include the following.

-   -   Type of Retirement Account (ROTH/IRA/SEP)    -   Beneficiary Name    -   Beneficiary Address    -   Primary or Contingency    -   Relationship to Beneficiary    -   Tax ID #    -   Date of Birth    -   Percentage of Beneficiary    -   Witness per state    -   IRA Check Writing    -   Authorization and acknowledgement of terms and conditions

ACH data: According to certain embodiments, some non-limiting examplesof ACH account data include the following:

-   -   Account Type—Individual, Joint, Custodial, Retirement (IRA/Roth        IRA/401k/Sep IRA), Entity (LLC, S CORP, C CORP, Investment Club,        Partnership)    -   Bank Name    -   Account Name    -   Account Type    -   ABA Routing number    -   Account Number    -   Dividends?    -   Occurrence (Monthly/Weekly/Quarterly/Semiannual/Annual)    -   Start Date    -   End Date    -   On Demand    -   Authorization and acknowledgement of terms and conditions

Investment Access data: According to certain embodiments, somenon-limiting examples of investment access data include the following.

-   -   Checking    -   To be printed on Check—(Telephone Number/Address/Drivers        License)    -   Visa Gold    -   Mothers Maiden Name    -   Authorization and acknowledgement of terms and conditions

Certification of Investment Power (Corp) data: According to certainembodiments, some non-limiting examples of certification of investmentpower (Corp) data include the following.

-   -   State of Incorporation    -   Year of Incorporation    -   Tax Id Number    -   Enter in Transactions to:        -   Buy        -   Sell        -   Convey        -   Pledge        -   Mortgage        -   Lease        -   Transfer Title or otherwise acquire or dispose of interest            in real or personal property including with limitation            stocks, bonds, notes, warrants, annuities, futures,            currencies, commodities        -   Exceptions______    -   Pledge Securities (yes/No)    -   Allow Margin (Yes/No)    -   Allow:        -   Options        -   Covered Call writing/Protective Put        -   Cash Back Puts        -   Purchasing Puts/Calls        -   Spreads        -   Naked Call/Put        -   Other______    -   Authorization and acknowledgement of terms and conditions        -   1. Name and address        -   2. Account owner signature        -   3. Title (e.g., President, Partner, Trustee, Custodian)        -   4. Phone number        -   5. Occupation        -   6. Employer        -   7. Financial advisor signature

Certification of Investment Power (Trust) Data: According to certainembodiments, some non-limiting examples of certification of investmentpower (Trust) data include the following.

-   -   Date of Trust    -   Tax Id number    -   Name of each Grantor/Settler/Plan Sponsor    -   Date of Latest Trust Plan Amendment    -   Enter in Transactions to:        -   Buy        -   Sell        -   Convey        -   Pledge        -   Mortgage        -   Lease        -   Transfer Title or otherwise acquire or dispose of interest            in real or personal property including with limitation            stocks, bonds, notes, warrants, annuities, futures,            currencies, commodities        -   Exceptions______    -   Pledge Securities (yes/No)    -   Allow Margin (Yes/No)    -   Allow:        -   Options        -   Covered Call writing/Protective Put        -   Cash Back Puts        -   Purchasing Puts/Calls        -   Spreads        -   Naked Call/Put        -   Other______    -   Authorization and acknowledgement of terms and conditions        -   1. Name and address        -   2. Account owner signature        -   3. Title (e.g., President, Partner, Trustee, Custodian)        -   4. Phone number        -   5. Occupation        -   6. Employer        -   7. Financial advisor signature

RIA data: According to certain embodiments, some non-limiting examplesof RIA data include the following.

Account Information

-   -   Name    -   Account Number    -   Duplicate IRA form    -   Firm Name/Firm ID    -   Eligible Asset Value$______    -   Annual Fee______%    -   Payment Method—(Automatically deduct from account/Bill Directly        to client/Deduct form another Account______    -   Billing Cycle        -   Cycle 1—January, April, July, October        -   Cycle 2—February, May, August, November        -   Cycle 3—March, June, September, December        -   Authorization and acknowledgement of terms and conditions

Risk Profile Questionnaire

-   -   Advisory account represents what percentage of total investable        assets (Less than 20%, 21%-50%, 51%-75%, 76%-100%)    -   When do you expect to begin withdrawing significant funds from        account (Less than 1 year, 1-2 yrs, 3-4 yrs, 5-7 yrs, 8-10 yrs,        11+ yrs)    -   Once you begin withdrawing, how long do you expect portfolio to        last (Lump sum distribution, 1-2 yrs, 3-4 yrs, 5-7 yrs, 8-10        yrs, 11+ yrs    -   Portfolio selection regarding inflation (pick one)        -   Portfolio 1—likely exceed LT inflation by significant margin            and has high degree of volatility        -   Portfolio 2—likely exceed LT inflation by moderate margin            and has high degree of volatility        -   Portfolio 3—likely exceed LT inflation by small margin and            has small to moderate degree of inflation        -   Portfolio 4—likely match inflation and has low degree of            volatility    -   Hypothetical Portfolio with average return, probability of        higher value and lower value after 1 yr (pick one)

$105,000 83% 17% $106,200 79% 21% $107,400 76% 24% $108,500 73% 27%$109,500 71% 29%

-   -   Investment Goals (pick one)        -   Protect value of portfolio, accept lower LT returns.            Conservative        -   Minimum Risk, try to achieve slightly higher returns.            Conservative        -   Balance moderate level of risk with moderate level of            returns        -   Maximize LT returns, accept dramatic ST fluctuations in            value    -   Reaction to a 20% short term loss, consistent with market (pick        one)        -   Not change portfolio or invest more in portfolio        -   Wait at least 1 yr before changing to more conservative        -   Wait at least 3 months before changing to more conservative        -   Immediately change to more conservative    -   Graph of hypothetical portfolios with potential gains, expected        potential, and potential loss (pick one)    -   Preliminary Risk Profile score    -   Profile Customization. Reason for customization    -   Final Risk Profile Selection

Tax data: According to certain embodiments, some non-limiting examplesof tax data include the following.

W-9

-   -   Check if subject to backup withholding    -   Check if presently applying for Taxpayer ID    -   Check if you are exempt from backup withholding and information        reporting    -   Internet gambling Attestation (required for all entity accounts)    -   Authorization and acknowledgement of terms and conditions    -   Disclosure of Account Information—    -   Under the SEC Rule 14B-1(c), we will be obligated to provide        your name, address and securities positions to each requesting        company whose securities we hold for your account unless you        object to such disclosure.    -   If you object______

W-8

-   -   Name    -   Type Of Beneficial Owner        -   Individual        -   Corporation        -   Disregarded Entity        -   Partnership        -   Simple/Grantor/Complex Trust        -   Estate        -   Government        -   International Organization        -   Central Bank of issue        -   Tax Exempt organization        -   Private Foundation    -   Permanent Address    -   Mailing Address    -   US Taxpayer ID (if required)    -   Foreign Taxpayer ID if any    -   Certification that: (check all that apply)        -   The beneficial owner is a resident of______        -   If required, the US Tax ID is stated above        -   The beneficial owner is not an individual, derives the            item(s) of income for which the treaty benefits are claimed        -   The beneficial owner is not an individual, is claiming tax            treat benefits for dividends received from a foreign            corporation        -   The beneficial owner is related to the person obligated to            pay income within the meaning of section 267(b)

Transactional data: According to certain embodiments, some non-limitingexamples of transactional data include the following.

-   -   Firm/Branch identifier    -   Account Number    -   Marginable (Exchange listed marginable, not marginable OTC,        marginable OTC, no info)    -   Account Type (street side account, cash, margin, when issued,        dividend/interest account, non-purpose loan account, short        margin account, special subscription account, convertible bond        account, non-convertible bond account, COD cash on Delivery        account, COR cash on receipt account)

EQUITIES (Stocks, Warrants, Preferred,)

Traded on the following market centers

-   -   1. ArcaEdge,    -   2. BATS BYX (BYX)    -   3. BATS Global Markets (BTRADE)    -   4. Bloomberg Tradebook (BTRADE)    -   5. CBOE Stock Exchange (CBSX)    -   6. Chicago Stock Exchange (CHX)    -   7. DIRECTEDGE(DRCTEDGE)    -   8. DIRECTEDGE(EDGEA)    -   9. IB VWAP Dealing Network(VWAP)    -   10. Instinet    -   11. INET(Island)    -   12. ISE Stock Exchange (ISE)    -   13. Knight Securities,    -   14. LaveFlow ECN(LAVA)    -   15. NASDAQ(NASDAQ)    -   16. NASDAQ OMX BX (BX)    -   17. NASDAQ OMX PSX(PSX)    -   18. National Stock Exchange(NSX)    -   19. New York Stock Exchange (NYSE)    -   20. NYSE AMEX (NYSE, AMEX)    -   21. NYSE Arca(ARCA)    -   22. Pink OTC Markets(PINK)

Trade Date Settlement Date

-   -   As of Date    -   Security Number    -   CUSIP    -   Quantity    -   Price    -   Buy or Sell    -   Cancel Rebill indicator    -   Blotter Code    -   Discretion Exercised (yes, No)    -   Accrued Date    -   Principal    -   SEC Fee    -   Commission    -   Net Amount    -   Symbol    -   Special Tax Indicator    -   Discount    -   Commission Codes    -   Handling Fee    -   Basis    -   Security Subtype    -   Sales Credit Code and Amount    -   Nasdaq    -   Order Number    -   Execution Time    -   Market Price    -   State Tax    -   Credit Interest

ETF (Exchange Traded Funds)

-   -   Traded on the following market centers        -   1. CBOE Stock Exchange (CBSX)        -   2. Chicago Stock Exchange (CHX)        -   3. NASDAQ OMX BX (BX)        -   4. National Stock Exchange(NSX)        -   5. New York Stock Exchange (NYSE)        -   6. NYSE AMEX (NYSE, AMEX)        -   7. NYSE Arca(ARCA)    -   Trade Date    -   Settlement Date    -   As of Date    -   Security Number    -   CUSIP    -   Quantity    -   Price    -   Buy or Sell    -   Cancel Rebill indicator    -   Blotter Code    -   Discretion Exercised (yes, No)    -   Accrued Date    -   Principal    -   SEC Fee    -   Commission    -   Net Amount    -   Symbol    -   Special Tax Indicator    -   Discount    -   Commission Codes    -   Handling Fee    -   Basis    -   Security Subtype    -   Sales Credit Code and Amount    -   Nasdaq    -   Order Number    -   Execution Time    -   Market Price    -   State Tax    -   Credit Interest

FIXED INCOME (Corporate Bonds, Treasuries, CMO, FNMA, GNMA, UITs &Municipal Bonds)

-   -   Traded on the following market centers        -   1) Bond Desk        -   2) Knight BondPoint        -   3) Knight BondPoint for Munis        -   4) Knight BondPoint for US Government Securities        -   5) MuniCenter        -   6) NYSE Arca Bonds (NYSE BONDS)        -   7) Timber Hill Auto-Ex Bonds        -   8) TradeWeb        -   9) TradeWeb for Munis        -   10) TradeWeb for US Goverment Securities    -   Factor    -   Muni-CB    -   Accrued Interest    -   Sales Credit Code and Amount    -   Trade Date    -   Settlement Date    -   As of Date    -   Security Number    -   CUSIP    -   Quantity    -   Price    -   Buy or Sell    -   Cancel Rebill indicator    -   Blotter Code    -   Discretion Exercised (yes, No)    -   Accrued Date    -   Principal    -   SEC Fee    -   Commission    -   Net Amount    -   Symbol    -   Special Tax Indicator    -   Discount    -   Commission Codes    -   Handling Fee    -   Basis    -   Security Subtype    -   Sales Credit Code and Amount    -   Order Number    -   Execution Time    -   Market Price    -   State Tax    -   Credit Interest

Options

-   -   Traded on the following market centers    -   NYSE Amex (NYSE AMEX)    -   Chicago Board of Exchange (CBOE)    -   Pacific Stock Exchange (PSE)    -   NASDAQ OMX (NASDAXOM)    -   Philadelphia Stock Exchange (PHLX)    -   International Stock Exchange (ISE)    -   BATS Global Markets (BATS)    -   Boston Options Exchange(BOX)    -   CBOE C2 (CBOE2)    -   Option Type (Put, Call)    -   Underlying Security (for Options)    -   Strike Price    -   Expiration Date (Month & Year)    -   Trade Date    -   Settlement Date    -   As of Date    -   Security Number    -   CUSIP    -   Quantity    -   Price    -   Buy or Sell    -   Cancel Rebill indicator    -   Blotter Code    -   Discretion Exercised (yes, No)    -   Accrued Date    -   Principal    -   SEC Fee    -   Commission    -   Net Amount    -   Symbol    -   Special Tax Indicator    -   Discount    -   Commission Codes    -   Handling Fee    -   Security Subtype    -   Order Number    -   Execution Time    -   Market Price    -   State Tax    -   Option level code    -   1. Covered Equity/Index Call Writing    -   2. Purchasing Equity/Index Puts against a Long Stock Position    -   3. Cash-Backed Equity/Index Put Writing    -   4. Purchasing Equity/Index/Foreign Currency Puts and Calls    -   5. Equity/Index Spreads    -   6. Equity/Index Put Writing on Margin    -   7. Uncovered Equity/Index Call Writing    -   8. Writing Equity/Index Combinations/Straddles

Mutual Funds

-   -   Mutual Fund Share Class    -   1. Front-end load with a sales charge greater than 4%    -   2. Back-end load subject to a contingent deferred sales charge        (CDSC)    -   3. Level load    -   4. Closed-end fund traded like stock (security type C for common        stock)    -   5. Exchange Traded Funds (security type C for common stock)    -   6. Low front end load with a sales charge of less than 2%    -   7. Multi-class fund that allows exchanges into or out of        different share classes    -   8. Mid-front-end load with a sales charge of 2-4%    -   9. No front- or back-end load, although 12b-1 fees may apply    -   Prospect Required    -   Sales Credit Code and Amount    -   Trade Date    -   Settlement Date    -   As of Date    -   Security Number    -   CUSIP    -   Quantity    -   Price    -   Buy or Sell    -   Cancel Rebill indicator    -   Blotter Code    -   Discretion Exercised (yes, No)    -   Accrued Date    -   Principal    -   SEC Fee    -   Commission    -   Net Amount    -   Symbol    -   Special Tax Indicator    -   Discount    -   Commission Codes    -   Handling Fee    -   Basis    -   Security Subtype    -   Sales Credit Code and Amount    -   Order Number    -   Execution Time    -   Market Price    -   State Tax    -   Credit Interest

Futures

-   -   Traded on the following market centers    -   1. CBOE Futures Exchange(CFE)    -   2. CBOT (ECBOT)    -   3. CBOT (Floor-Based)    -   4. CME (Electronic-Globex)    -   5. CME (Floor-Based)    -   6. ELX Futures (ELX)    -   7. New York Mercantile Exchange (NYMEX)    -   8. New York Board of Trade (NYBOT)    -   9. NYSE Liffe (NYSELIFFE)    -   10. ICE Futures US (ICEUS)    -   11. OneChicago (ONE)    -   Trade Date    -   Settlement Date    -   As of Date    -   Security Number    -   CUSIP    -   Quantity    -   Price    -   Buy or Sell    -   Cancel Rebill indicator    -   Blotter Code    -   Discretion Exercised (yes, No)    -   Accrued Date    -   Principal    -   SEC Fee    -   Commission    -   Net Amount    -   Symbol    -   Special Tax Indicator    -   Discount    -   Commission Codes    -   Handling Fee    -   Basis    -   Security Subtype    -   Sales Credit Code and Amount    -   Order Number    -   Execution Time    -   Market Price    -   State Tax    -   Credit Interest

Managed Accounts—Advisor

-   -   Traded on the following market centers        -   1. ArcaEdge,        -   2. BATS BYX (BYX)        -   3. BATS Global Markets (BTRADE)        -   4. Bloomberg Tradebook (BTRADE)        -   5. CBOE Stock Exchange (CBSX)        -   6. Chicago Stock Exchange (CHX)        -   7. DIRECTEDGE(DRCTEDGE)        -   8. DIRECTEDGE(EDGEA)        -   9. IB VWAP Dealing Network(VWAP)        -   10. Instinet        -   11. INET(Island)        -   12. ISE Stock Exchange (ISE)        -   13. Knight Securities,        -   14. LaveFlow ECN(LAVA)        -   15. NASDAQ(NASDAQ)        -   16. NASDAQ OMX BX (BX)        -   17. NASDAQ OMX PSX(PSX)        -   18. National Stock Exchange(NSX)        -   19. New York Stock Exchange (NYSE)        -   20. NYSE AMEX (NYSE, AMEX)        -   21. NYSE Arca(ARCA)        -   22. Pink OTC Markets(PINK)    -   Trade Date    -   Settlement Date    -   As of Date    -   Security Number    -   CUSIP    -   Quantity    -   Price    -   Buy or Sell    -   Cancel Rebill indicator    -   Blotter Code    -   Discretion Exercised (yes,)    -   Accrued Date    -   Principal    -   SEC Fee    -   Net Amount    -   Symbol    -   Special Tax Indicator    -   Discount    -   Basis    -   Security Subtype    -   Annual Advisor Fee $    -   Annual Manager Fee $    -   Annual Advisor Fee %    -   Annual Manager Fee %    -   Name of Manager    -   Confirms Monthly (Y/N)    -   Order Number    -   Execution Time    -   Market Price    -   State Tax    -   Credit Interest

Private Placements

-   -   Issuer Name    -   Issuer Status—Public or Private    -   Symbol    -   CUSIP    -   Exchange where traded    -   Issuer Location    -   Issuer Industry    -   Issuer Market Cap on date of Investment    -   Investment use (Seed/Venture/Startup/Growth Capital/Refinance of        debt/General Working Capital/Acquisition    -   Investment Type (Private Placement in Private Company, Private        Placement in Public Company, IPO, Secondary Offering, Registered        Direct Offering, Rights Offering)    -   Total Offering Amount $    -   Unit Size    -   Price Per Unit    -   Shares per Unit    -   Security Type Purchased        -   1. Common Stock            -   a) Price Per Share        -   2. Convertible Preferred Stock            -   a) Convertible Price            -   b) Yield            -   c) Term

Behavioral data: According to certain embodiments, some non-limitingexamples of behavioral data include the following.

-   -   Time/Date/Patterns of Transactions    -   Securities in Portfolio    -   Securities researched    -   Time/Date/Patters of incoming fund transfers and type of        transfer    -   (Wire/ACH/ACAT/Check/Check writing/Debit    -   Time/Date/Pattern/payee information of bill payments from        account    -   Securities transferred in and out and method (DTC, ACAT, etc . .        . )    -   Credit and Debit Card Transactions

Credit check and address change: In addition, the brokerage systemcollects and stores credit check results and address change informationassociated with the customers/users of the brokerage system for purposesof data analytics and monetization, according to certain embodiments.

Data Analytics

Various data elements are organized before applying techniques of dataanalytics. A financial brokerage system has many types of informationabout the customer/user of the financial brokerage system. Some of theinformation is gathered at the time of registration and othersaccumulated over time based on the types of transactions the customerperforms at the web site. For example, all the activity of the customeron the online financial brokerage system over time including time andfrequency of visits to the online financial brokerage system will becaptured by the financial brokerage system. In addition, the financialbrokerage system can take advantage of data from outside sources tolearn more about customer expectations and behavior. This may requireworking out the right agreements with partners for promotions andadvertisements. For example, such data can include the purchase behaviorof certain items like cars or insurance from ad networks or car dealers,for a profile of users that are potential buyers of such items that thefinancial brokerage system can identify with its own data. Otherexamples of data sources can include news feeds, court electronicdatabases (PACER), Department of Motor Vehicles, Internet, communitymultimedia centers, social networks, etc. In other words, the data fromdata sources outside of the financial brokerage system can be used inconjunction with brokerage data for purposes of data analytics. The dataorganization and analytics techniques and monetization objectives of thefinancial brokerage system can inform on the types of combinations ofoutside data sources with data from the financial brokerage system formonetization. The following non-limiting examples illustrate some usefuldata analytics of brokerage data in combination with other sources ofdata for predicting behavior or for making suggestions or offers to thecustomer of the financial brokerage account or for sharing informationwith various industry partners for purposes of monetization of data. Thefollowing examples are merely illustrative and are for purposes ofexplaining the concept of combining various sources of data withbrokerage information in order to generate revenue for the financialbrokerage company and to return value to the customer.

Monitor Online Portals Example

UNESCO and Al Jazeera to promote freedom of expression in the ArabWorld.+marital status of customer (married)+joint account+H income ofcustomer (high)+of customer net worth (high)+W8 (non US Citizen fromEgypt)+address of customer (indicating US main address)+4 dependents ofcustomer+direct deposits into 3 custodial savings accounts ofcustomer+address of customer=a donation to United Nations EducationalScientific and Cultural Organization.

Monitor Major News Outlets Current Events Example

The Pope visits Cuba, greeted by Raul Castro and later meets with FidelCastro.+zip code (Miami Florida high latino density area)+income ofcustomer+net worth of customer+wire transfer requests (to The CubanAmerican National Foundation)+account log in of customer and tradingrecords of customer+occupation of customer (owns religious book store orprofessor at Christian school)=donation to Church (donation size variesbased on income and net worth fields).

News example: batteries in electric cars are catching on fire+customercame to our site after visiting a hybrid car web site=change to purchaseHybrid car for customer

Entertainment Industry News Example

Justin Bieber is playing in LA+high net worth of customer+high income ofcustomer+zip code+Occupation of customer (attorney for ColumbiaRecords)+2 custodial accounts of customer+age of custodian (11 and 13years of age)+birthday of custodian around time of concert=purchaseJustin Bieber tickets.

News of a New President, a More Liberal Vs. Conservative One

An anti war pro social programs president+income of customer+net worthof customer+zip code of customer+occupation of customer+tax bracket ofcustomer+customer has defense stocks in portfolio=reduce and or sellmilitary stocks in your portfolio.

Monitor Public Access to Court Electronic Records (PACER) www.pacer.gov“PACER is an electronic public access service that allows users toobtain case and docket information from federal appellate, district andbankruptcy courts and the PACER Case Locator via the internet.

Example

Court ruling deciding in favor of a challenge to a recent board decisionin some town USA to only allow 1 house per 5 acre zoning instead of whathad always been 1 house per 2 acre zoning.+gender of customer(male)+marital status of customer (married)+income of customer+net worthof customer+3 dependents+zip code of customer+occupation of customer(owns residential construction company)+college savings account ofcustomer+also has business account (residential builders)+applied forspecial purpose business loan to build a 50 unit residential complex (weknow this because he either applied for the loan through us or the bankhe is getting the loan from is contacting us to verify the assets in hisaccount held with us)+interest rates reported by Yahoo finance to be lowfor years=building 100 residential units instead of 50.

Monitor Driving Records Online. e-DMV.org Example

3 speeding tickets of customer, one of which was reckless driving and asuspended license+income of customer+net worth of customer+date of birthof customer (birthday in 1 week)+zip code of customer+investmentobjectives of customer (preservation of capital shows the person isconservative)+marital status of customer+bought tickets online from ourpoints program for Scotch tasting event on birthday or visited Scotchtaste testing site prior to or after coming to our website=needs a carand driver.

Example

Recent DWI of customer+marital status of customer (married)+income ofcustomer+net worth of customer+current date (New Years Eve)+zip code ofcustomer+3 dependents=rent limousine.

Monitor FINRA broker check—financial industry regulatory authority.FINRA BrokerCheck—is a free online tool to help investors check theprofessional

-   -   http://www.finra.org/Investors/ToolsCalculators/BrokerCheck

Example

Customer's investment broker (who has multiple infractions includingunauthorized trades)+marital status of customer+income of customer+networth of customer+zip code of customer+investment objectives ofcustomer+address of customer+current date+(all fields filledout)=transfer account to new financial brokerage firm

Monitor Weather report Example

News of a severe storm headed in customer's direction (area NewOrleans)+marital status of customer+income of customer (low)+net worthof customer (low)+customer owns home+2 dependents+zip code of customer(lower 9th ward)=purchase of ply wood to board up windows.

Example

News of a severe storm headed in customer's direction (area NewOrleans)+marital status of customer+income of customer (high)+net worthof customer (high)+customer owns home+2 dependents+zip code of customer(affluent suburb of New Orleans)=suggest purchase upscale hotel room fortwo weeks.

News of a severe storm headed in customer's direction (area NewOrleans)+marital status of customer (single)+income of customer(unemployed)+net worth of customer (high)+customer rents+zip code(apartment on Bourbon street)=take two week vacation in Jamaica (Jamaicabecause of previous intelligence gathered by financial brokerage system)

Customer Looking to Refinance Home Example

Public records show, customer paid 100 k for house+credit report (statesbalance of loan 85 k)+indicates down payment below 20% onpurchase=mandatory PMI (private mortgage insurance)+zillow appraisal(value of house)+lower interest rates+value of all accounts held bycustomer+zip code of customer+income of customer+net worth ofcustomer+conservative investment objectives of customer+refinancing=Newrefinance (valuable to mortgage brokers also because of knowledge thatmortgage brokers can now save the customer additional money byeliminating the PMI)

An important feature of monetizing customer behavior is the creation ofone or more profiles of the customer of the financial brokerage system.The profile describes the customer's attributes. The profile can be usedto provide the right advertisement or promotion when the customer islogged in. The profile can also be used to provide a list of customersto ad networks or other partners who are interested in offering somespecial promotions. New profiles can be created easily based on partnerrequirements. For example, a car dealer may have certain criteria thatthe profiles need to satisfy. For this level of flexibility, one or moretaxonomies are created to classify the financial brokerage systemcustomers for different profile needs.

The individual profile can be provided to marketers at differentgranularities to target advertisements. Profile clusters will be usefulto marketers for making special promotions or offers. The financialbrokerage system can give marketers the list of customers that fit intospecific profile clusters with a measure of confidence (for example,1-10). Further, marketers can work the financial brokerage system tocreate special profiles. The taxonomy and individual profiles willfacilitate quick and easy way of creating new profile clusters.

Another feature of data analytics is around clusters of profiles.Profile clusters identify groups of customers/users that fit somespecific profiles of interest.

Further, rules are generated to apply the individual profiles,attributes in taxonomy or profile clusters to test and learn customerbehavior and thus predict future behavior. The rules can be used inmultiple ways:

-   -   Special promotion for those that satisfy certain rules    -   When a rule gets triggered, inform marketers regarding the        change to make special offers    -   Use the trigger for internal use within the financial brokerage        for promoting other products

Methods of data mining include but are not limited to customer/userclassification taxonomy, individual customer/user profile generation,profile cluster development and rules generation, regression analysisand learning & prediction.

Customer Classification Taxonomy: Create taxonomy that helps classifycustomers for different characteristics. A customer can belong tomultiple nodes in the taxonomy. The taxonomy can be used to createindividual profiles for quick access and to generate lists of customersthat meet specific characteristics. The taxonomy is created afterreviewing the different customer data items in the financial brokeragesystem. The following is a non-limiting example of a taxonomy. Thetaxonomies are based on the objectives of the financial brokerage systemand can vary from implementation to implementation.

Gender   Male   Female Age    < 30 years    30 - 40    40 - 50    50- 60   60 -70    > 70  Marital Status   .....     .......  Annual income    < $100K     .......  Net worth      < $500K   $500K - $1M     ..... Occupation     ......  Zip Code   Average household income:    < $500K    $500K - $1M     ....   Or Average house value:....     ...... Investment Objective     Preservation of Principal     ConservativeGrowth     Aggressive     Speculation     ......  Risk Profile     ..... Spouse’s Occupation     .....  Spouse’s Age     .....  Country ofcitizenship     .....  Tax bracket     .......  Employer Zip Code    .......  Stocks     .....  Trading style     Daily     Weekly    Monthly     .....  Children     Elementary school      1      2     3      > 3     Middle school      1      2      ....     Highschool      ...     College     ....   ....

Individual Customer Profile: For each customer all available informationare stored (both internally available from the customer's use of thefinancial brokerage system and gathered from outside sources) in theprofile. The data associated with the customer classification taxonomyare stored in the profile in the format that matches the nodes in thetaxonomy. This taxonomy related information will be stored to matchprofile queries in an efficient manner.

Profile Clusters: Clusters are special profiles that match certaincharacteristics of interest to marketers, for example, based on industrybehavior. Each cluster can have its own identifier or name. Eachcustomer can be classified under multiple clusters. The followingcharacteristics are merely examples for purposes of explanation.

-   -   Buyer of a security XYZ    -   Customers who login into account every day    -   Logs into account 30 minutes prior to market close and 30        minutes after market close    -   Consistently buying low priced securities    -   Age>X, Net worth>Y, Children in high school>1    -   . . .

Rules: Data analytics includes defining rules that predict behaviorbased on individual customer profile or profile clusters. The system cantrigger alerts or action when the rules are satisfied. The followingrules are merely examples for purposes of illustration.

-   -   a. A custodian on a custodial account (from individual profile)        −>buys things for the children, cares for the child    -   b. With power of attorney and age −>X−> Need for assisted living    -   c. Those buying low priced securities or deposit low priced        securities −> likely to invest in high risk instruments

Learning and Prediction

Customers can be classified for different partner needs or behaviorprediction by learning through rules and clustering. Partners of thefinancial brokerage system can include commercial retailers, banks,advertising agencies, insurance companies, realtors, mortgage brokers,airline industry, entertainment industry, hospitality industry,recreation industry, etc. Both associative rules and decision trees willbe used. Regression also will be used for learning and prediction.

The techniques described herein (rules and decision trees forclassifications, clustering and regression) can be used for predictiveanalytics. Such techniques can be used when new accounts are created andwhen attributes for existing accounts change. The expected behavior of anew customer that fits into certain profile (e.g., a single parent withsmall children who just changed address will be expected to look for daycare services) can be predicted using the rules that have been createdin the system. Similarly when an attribute of the customer changes, thecustomer may fall into a new cluster and a new set of rules will applyto predict certain behavior. For example, for a customer that crosses acertain age (e.g., 65 years of age), new offers can be provided to thiscustomer based on the rules in place for the new cluster.

Rules Learning: Rules are used in learning systems to predict behavior.The rules are looked up for the right conditions to decide the outcome.The antecedent or precondition of a rule is a series of tests, while theconsequence or conclusion give the class or classes that apply toinstances covered by the rule, or sometimes a probability distributionover the classes. The preconditions are logically ANDed together, andall the tests must succeed in order for the rule to trigger.

Examples of decision tree: The following is merely an example forpurposes of illustration.

Net worth −> Age range −> Number of children => expected educationexpense Has power of attorney −> age of account holder => need forassisted living Married Status  Single   Income ( > $x)    Number ofdependents (> 0)     Custodian Account (Yes)      Age of children (< 12)      Use day care center  Married   Income ( > $Y )    Number ofdependents ( >0 )     Custodian Account ( Yes )      Age of children ( 5-18 )       Use private school

Association rules: The following association rules are examples forpurposes of explanation.

-   -   1. Customer buys low priced securities=>high probability of        investing in high risk investments    -   2. Customer purchases security in portfolio=>owner of said        security    -   3. Customer is custodian on a custodial account=>buys things for        children

Initial set of rules are created manually based on expertise andhypotheses. These rules can be created as decision trees or tables. 75%of existing information can be used for preparing these rules. This datais the training data set. The remaining 25% of the data is used fortesting the rules. For example, consider the following sample rules:

Single+income+net worth+change of address+number of dependents+custodianaccount+age of minor=need for new day care center.

Married+high income+high net worth+change of address+number ofdependents+custodian account+age of minor=need new private school

The data values at each decision point for the above are created usingthe training data set and validated using the remaining data. Thecorrelation results can be measured and can be visually presented toverify the correctness of the rules.

Clustering: Clustering is the process of discovering groups andstructures in the data that are in some way “similar”, without usingknown structures in the data.

Hierarchical clusters using information about gender, profession, networth, number of children, stocks, etc., can be created. Probabilitybased clustering can be used. Clustering can be directed with differenttypes of seed data to evaluate the effectiveness of the resultingclusters. Seed data are initial sets of data to act as centroids of thedifferent clusters. As a non-limiting example, if clusters are to bedirected by net worth followed by investment risk profile, thenclustering can be initiated with chosen sets for net worth and possiblyrandomly chosen investment risk profiles. Through multiple iterations,clustering will stabilize around centroids.

Classification: Classification is the process of identifying groups orrules to apply for new data items based on rules already created usingexamples. The financial brokerage system can use both association rulesand decision trees for classification. For decision trees, one rule isgenerated for each leaf. The rules will be unambiguous in that the orderof execution of the rules is irrelevant. To classify customers intodifferent classes, association rules can be used.

When new customer data is entered into the financial brokerage system,the classification rules will be applied to identify the classes towhich the customer belongs. For example, by using the decision trees,the customer can be classified as one needing day care center, need forassisted living, etc. Similarly, the association rules can be used toclassify customers as high or low risk investors, those who buy thingsfor children, etc.

Regression: Regression can be used for numeric data that fit statisticalfunctions. Most commonly used techniques involve linear regression wherethe right fitting function can be derived using statistical packages.The idea is to express the class as a linear combination of attributes,with pre-determined weights. For example, one situation where regressioncan be applied is to find a pattern between the number of transactionsby the financial brokerage customers and login time before and aftermarket close by the customers. If there is a relationship, one canpredict the number of transactions for those customers logging in to thefinancial brokerage system at certain periods of time.

Some non-limiting examples of using data from external sources forpredicting potential behavior and thus monetize the customer profilesalready created are given below:

-   -   1. Profile clusters of profession (farmers) with specific        investment risk profile (conservative investment portfolio) for        an income range (above $250K) and owning some specific stock        (owns Ford stock) can be expected to own Lincoln town cars. The        output result (own Lincoln town cars) can be verified using data        obtained from business partners of the brokerage system. This        combination of brokerage data and data from a business partner        of the financial brokerage system can be used to monetize this        type of profile.    -   2. External news feeds can be monitored to trigger specific        promotion events. Profile clusters of customers in certain zip        codes (Miami) and income range with some specific occupation of        the customer (own religious book store or professor at Christian        school) can be expected to be willing to contribute to church in        Cuba. Any news related to churches in Cuba could be of        importance to this profile cluster, as an example. For example,        news about the Pope visiting Cuba or Raul Castro announcing more        religious freedom can be used to trigger a contact with        organizations interested in contacting customers in the profile        to request contributions.    -   3. As another example, the group of those customers who own        electric cars (using the rule if the customer owns electric        cars) can be used by advertisers on request or when news comes        out about electric cars (e.g., battery catching fire) to inform        the customers in the group about potential deals on hybrid cars.        The latter can be further enhanced by monitoring the web sites        the customer visited before coming to this site. If the customer        logs in after visiting a hybrid car web site, the probability of        his/her interest in a hybrid car is much higher.

Summarization

It is important to present the results of data analytics in a way thatis easy to understand and that summarizes the results for the targetaudience. Different reports and charts and graphs can be prepared usingstandard reporting packages. Some sample reports can include the numberof new customers that fit within the different classifications orprofiles and the trends over a period of time. Another report can be thesuccess rate of the different rules for classifications.

Heat map is another effective visual presentation to show datarelationships in multiple dimensions. A heat map presents data withinclasses (or matrix) as colors. The size of each matrix represents therelative importance one attribute and the color can represent therelative importance of another attribute. For example, a representationof clusters around net worth in a heat map can represent the size ofeach cluster by the size of the rectangle and the color can indicate thenumber of transactions performed by each cluster. Red can indicate highactivity and yellow low activity. Visually, such a representation iseasy to understand and can easily identify which clusters have the mostactivity.

Processes

After the initial data models (classification taxonomy, rules, clustersand regression functions) are created, the system needs to be maintainedon an on-going basis.

The initial taxonomy can be created manually by reviewing the customerdata by the financial brokerage system internal experts. This can beenhanced over a period of time as the system learns more about customerbehavior patterns and if higher degrees of granularity of profiles areneeded.

Once the taxonomy is created individual profiles can be derived from it.An automated process can maintain the profiles on a regular basis. Thiswill be a matter of looking up data values for each node in the taxonomyand keeping a bit pattern for the profile.

Clusters are created in different ways. Using the data of known industrybehavior of interest of customers (those buying high risk instruments,those who put on a hedged position, etc.) a set of industry behaviorclusters will be created.

Another set can be clusters of interest for predicting industry behaviorof known groups or test hypotheses (those who log in into account 30minutes prior to market close and 30 minutes after market close likelyto perform transaction, those who check their account every day arelikely to put on a hedged position, those who buy only funds likely tostay only within those bonds, etc.).

Clusters for non-industry behavior are created using hypotheses and onmarketer's (or business partner of brokerage) request. Non-limitingexamples can include:

-   -   Financial brokerage customer is a custodian on a custodial        account (hypothesis: buys things for child, cares for child)    -   Financial brokerage customer with power of attorney and above        certain age (hypothesis: likely to need assisted living)    -   Financial brokerage customers with kids that are coming of        college age and customer has college fund (hypothesis: the        liquidation and spending of fund assets, Funds are likely to be        spent on college, room and board, transportation, textbooks,        clothes etc.)    -   Financial brokerage customer profession (e.g.. farmer) with        conservative investment portfolio (likely purchases of such a        customer: high degree of probability he/she will purchase crop        insurance, low octane gasoline, and higher rated tires)    -   Financial brokerage customers falling within certain income        ranges and with a certain number of children. Some changes in        members of these dusters will be of interest to marketers (e.g.,        in some of these dusters, a change of address may indicate a        need for new day care center or a need for private school). Sub        dusters like those with single parent families will help refine        the dusters.

Many of the clusters can be created automatically using theclassification rules and association rules. In addition K-meansclustering can be used to create new clusters using cluster centerschosen based on industry experience (as a non-limiting example, networth+age). The data set can be split for building the models as wellfor testing them. Results of special promotions or targetedadvertisements at these clusters will validate the hypothesis as well asrefine the clusters.

Initial set of rules are created manually. Profile clusters can be usedfor generating rules. Some of the rules will be used in conjunction withclusters to trigger events. An example will be change in address forthose in single parent with small children clusters needing new daycare. The system will alert this change which will be used for sendingspecial offer or promotion to the customer.

Creation of the rules and clusters is an iterative process. The data setaside outside of the training data set can be initially used forvalidations. For different rules and clusters, the expected precisionand recall values can be defined. Tests will show how well the modelswork for the required precision and recall values. The models can betuned to fit the expectation. The system can regularly monitor theeffectiveness of the prediction against the rules using the precisionand recall values as the guide.

New association rules can be identified as part of the learning process(e.g., a pattern about the timing of the change of address of singleparent family can predict such changes in the future and hence certainbehavior for purchases. As another example, some number of members of acluster of ‘risk investors’ tend to purchase some types of instrumentsat certain periods of time, etc.). Some rules that include data fromexternal sources may be difficult to validate, initially. But over aperiod of time enough data would be collected to validate the rules.Data provided by business partners of the financial brokerage system canbe used to improve the rules in such cases.

Profiles can be automatically generated for new customers andappropriate clusters can be identified. Promotions or special offers canbe made to these new customers. The new customers' behavior can betracked against the rules to increase learning and improve theprediction in the system.

Data that does not fall into any category through any of the methodslike classification, association, regression and clustering can be ofsignificance. Such data can be reviewed to see if the data is really ananomaly or better tuning of the classification models can help identifynew classes to fit such data. The precision and recall values inclassification and clustering can be adjusted to experiment. New refinedclusters can be created or rules can be modified for classification.This can be part of the on-going maintenance of the system.

The foregoing description, for purpose of explanation, has beendescribed with reference to specific embodiments. However, theillustrative discussions above are not intended to be exhaustive or tolimit the invention to the precise forms disclosed. Many modificationsand variations are possible in view of the above teachings. Theembodiments were chosen and described in order to best explain theprinciples of the invention and its practical applications, to therebyenable others skilled in the art to best utilize the invention andvarious embodiments with various modifications as are suited to theparticular use contemplated.

1. A computer-implemented method for monetizing financial brokerageaccounts, the method comprising: storing in an information repositoryinput data received online for a customer of a financial brokeragesystem; creating an individual profile for the customer describingattributes of the customer based on the input data of the customerstored in the information repository; automatically classifying thecustomer in one or more taxonomies based on the individual profile forthe customer, each one of the one or more taxonomies being subject to adistinct set of rules satisfying criteria of the financial brokeragesystem; receiving, for each of a plurality of users including thecustomer, respective first transactional data indicative of one or moretransactions involving the respective user and respective firstbehavioral data indicative of one or more behaviors of the respectiveuser; executing a clustering algorithm using the first transactionaldata and the first behavioral data of the plurality of users to generateone or more profile clusters; mining data from the individual profilefor the customer and the one or more taxonomies; executing aclassification algorithm using the mined data in real-time to classifythe customer in a first profile cluster of the one or more profileclusters, wherein the first profile cluster monetizes the mined data inreal-time to predict customer behavior subject to the distinct set ofrules; providing, via a website over the internet, the customer withfree trades on a display screen of a device, wherein the at least onecomputer module monetizes the mined data by performing data analyticsincluding automatically generating in real-time a new set of rules tolearn customer behavior and to predict subsequent customer behavior, thenew set of rules being generated in response to change in attributes ofthe customer; receiving, for the user, at least one of secondtransactional data or second behavioral data subsequent to predictingthe customer behavior; updating in real-time over the internet on anon-going basis at least one of the clustering algorithm or theclassification algorithm based on the predicted customer behavior andthe at least one of the second transactional data or the secondbehavioral data, wherein the updating includes modifying the set ofrules for the classification algorithm; and re-classifying, based on theat least one of the updated clustering algorithm or the updatedclassification algorithm, the customer in a second profile cluster. 2.The computer-implemented method of claim 1, wherein the free tradescomprises commission-free trades.
 3. The computer-implemented method ofclaim 1, wherein the free trades comprises eliminating a spread betweenbid and ask price.
 4. The computer-implemented method of claim 1,wherein the free trades comprises no load on mutual fund transactions.5. The computer-implemented method of claim 1, wherein the free tradescomprises eliminating principal's fees on fixed income securitiestransactions.
 6. The computer-implemented method of claim 1, wherein thefree trades comprises eliminating principal's fees on equity securitiestransactions.
 7. The computer-implemented method of claim 1, furthercomprising providing one or more of: free checking writing, and freebill payments.
 8. The computer-implemented method of claim 1, furthercomprising eliminating annual fees for the customer's account.
 9. Thecomputer-implemented method of claim 1, further comprising eliminatingtransfer fees for the customer's account.
 10. The computer-implementedmethod of claim 1, further comprising providing one or more of: no feefor debit card transactions, and no fee for credit card cash advances.11. The computer-implemented method of claim 1, further comprisingeliminating maintenance fees on the customer's retirement account. 12.The computer-implemented method of claim 1, further comprisingeliminating fees for market data services.
 13. The computer-implementedmethod of claim 12, wherein the market data services comprise: real timequotes, and streaming quotes.
 14. The computer-implemented method ofclaim 1, further comprising eliminating maintenance fees for employeestock option plans.
 15. The computer-implemented method of claim 1,further comprising eliminating wire transfer fees for the customer'saccount.
 16. The computer-implemented method of claim 1, furthercomprising eliminating ATM fees for the customer's account.
 17. Thecomputer-implemented method of claim 1, further comprising eliminatingACH fees.
 18. The computer-implemented method of claim 1, furthercomprising providing free trades to referral clients referred by thecustomer.
 19. The computer-implemented method of claim 1, furthercomprising: automatically updating in real-time the classification ofthe customer from a first taxonomy of the one or more taxonomies to adifferent second taxonomy of the one or more taxonomies based on changein the attributes of the customer; and predicting the second taxonomybased on the distinct set of rules.
 20. The computer-implemented methodof claim 1, further comprising: automatically integrating the new set ofrules into the distinct set of rules based on validation of the new setof rules over a period of time.